The built-in “Nlp Keyphrase Tagger” pipes items through a configurable SpaCy Pipeline to perform Key-Phrase Extraction and additionally Named Entity Recognition as well as Rule-Based Sentiment Analysis.

General Configuration

The Pipelet is configurable within the pipeline Editor.

PDF Approximation

To reduce processing time of big PDFs, consider only a subset of pages.

• process_pages :

  • dynamic : Chosen relative to document size (default)
    Take at least 10 pages, but at most √total_pages

  • all : Take all pages.

  • int : Take first N pages.

Language Support

Per default english (en_core_web_sm) and german (de_core_news_sm) models are installed on Squirro instances.

• Install additional language models, for example Japanese
  python -m spacy download ja_core_news_sm

• language_models : Update SpaCy language model mapping (the language code is expected to be found in facet language, see Language Detection ) .
  

  

Key-Phrase Extraction

Extract highest ranked key-phrases based on the TextRank algorithm.
Key phrases are selected and ranked from a pool of recognised Noun Chunks and recognised Named Entities per item.

Configuration

• tag_phrases: Enable / Disable key-phrase tagging

• tag_top_k_phrases: Amount of phrases to tag

  • tag_phrases: Enable / Disable key-phrase tagging

  • tag_top_k_phrases: Amount of phrases to tag

    • dynamic : Total amount of phrases selected relative to document size (between 20 - 70)

    • 10 : Take N highest ranked phrases as specified

  • tag_topics: Enable simple topic-tagging based on key-phrases

Enrichment

Key phrases are stored within the facet:nlp_tag__phrases.
The item’s Title is also added.

Application

• Content based auto-completion (type-ahead)

• Significant-terms aggregation on search results
  

Simple Topic Detection

With configuration tag_topics:True, the pool of ranked key-phrases is used to extract cleaned, deduplicated phrases referred to as “topics” (stored in facet:nlp_tag__topics).

Concept

- Filter steps:
  - Remove terms with POS ["ADJ", "DET", "PUNCT"]
  - Remove terms containing (almost) only number characters, like `33120x`
  - De-Duplicate:
      - Skip phrases that are also detected in NER-TAGS ["PRODUCT", "EVENT", "PERSON"] (configurable)
      - Skip phrases that contain terms from already stored "topics"
- Select 20 phrases evenly across all ranks (as determined via TextRank)

Named Entity Recognition

(Optional)
Store recognised entities within their corresponding facet, like .

Configuration

• tag_entities : Enable entity (NER) tagging.

• collect_entities : Specify NER tags to be added. (Check support on installed Label Scheme).

• tag_entities_per_type : Amount of entities (per type) to be added to their corresponding facet.

Enrichment

One facet per entity, like Location = [Europe, London]

Sentiment Analysis

Applies rule based sentiment analysis (vaderSentiment) that is specifically attuned to sentiments expressed in social media or domains like NY Times editorials, movie reviews, and product reviews.
It doesn’t require any training data but is constructed from a generalizable, valence-based, human-curated gold standard sentiment lexicon.

Configuration

• tag_sentiment : Enable rule-based sentiment tagging (for english language only)

Enrichment

• Overall Sentiment Label
  facet:sentiment_pretrained
  One sentiment label (neutral, positive, negative) per document.

  • Sentiment analysis is applied per sentence

  • Sentences with neutral sentiment are skipped

• Overall Sentiment Score
  facet:nlp_tag__sentiment_score
  Float value within [-1,+1]

• Sentiment Assessment
  facet:positive_terms, facet:negative_terms
  A sentiment phrase consists of the valence-term and it’s context. \

Examples

Positive Product Feedback

• Input

"The tech provides insight into unstructured email content, it allows me to truly understand the conversation between the business and our customers. The insight gained from this analysis is significantly deeper than cam be achieved from structured data analysis” ^{*Copied from Gartner}

• Output

{ 
'sentiment_pretrained': ['positive'], 
'positive_terms': ['truly understand', 'insight gained'], 
'negative_terms': [],
'nlp_tag__phrases': ['structured data analysis', 'unstructured email content' ]
}

→ That review showcases the combined insights gained through sentiment-assessment and key-phrase extraction.

Negative Feedback

• Input

“This was not a good experience”

• Output

{
'sentiment_pretrained': ['negative'], 
'positive_terms': [], 
'negative_terms': ['not a good experience']
}